Text embedding model family trained on weakly supervised pairs.
| 维度 | E5 | OpenAI Text Embedding 3 |
|---|---|---|
| Dimensions | 1024 | 1536 by default, 256 to 3072 selectable |
| License | MIT | Proprietary |
| Max tokens | 512 | 8191 |
| Open weights | Yes | No |
| Provider | Microsoft | OpenAI |
灰底行表示两者相同；有差异的行才是下方推荐结论的依据。
由上述差异按固定规则生成，不是模型生成的。
Choose E5 if…
已记录的属性中，没有任何一项显示它更有优势。
Choose OpenAI Text Embedding 3 if…
已记录的属性中，没有任何一项显示它更有优势。
model · Embedding Models
E5 is an embedding model family trained on large collections of weakly supervised text pairs, with the task described in the input so one model serves queries, passages and classification. It solves the problem that an embedding model trained on one kind of pair transfers poorly to another. It exists because describing the task in the text is a cheap way to make one model cover several retrieval settings.
model · Embedding Models
Text Embedding 3 is OpenAI's hosted embedding family, notable for letting the caller choose a shorter output vector than the model natively produces. It solves the storage problem directly: embedding indexes are sized by dimensions times vectors, so trimming 3072 dimensions to 512 or 256 shrinks the index substantially at a small quality cost. It exists because that trade is one the caller is better placed to make than the model author, and it removes the need to retrain to change it.
